Unveiling Spatiotemporal Differences and Responsive Mechanisms of Seamless Hourly Ozone in China Using Machine Learning - Jiachen Fan; Tijian Wang; Qingeng Wang; Mengmeng Li; Min Xie; Shu Li; Bingliang Zhuang; Ume Kalsoom (Remote Sensing)
Surface ozone (O3) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O3 in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to investigate spatiotemporal differences in O3 during multistage COVID-19, and the response of O3 variation to meteorology and emissions were explored using Shapley Additive Explanations (SHAP) and WRF-Chem. The results indicate that the optimized model demonstrated higher accuracy, with CV-R2 of 0.96–0.97 and RMSE of 4.58–5.00 μg/m3. Benefitting from the full coverage of the dataset, the underestimated O3 was corrected and hotspots of short-term O3 pollution events were successfully captured. O3 increased by 16.8% during the lockdown, with high values clustered in the north and west, attributed to the weakened urban NOx titration resulting from reduced emissions. During the control and regulation period, O3 levels declined year by year. O3 exhibited significant fluctuations in the Pearl River Delta but remained stable in western China, with both regions demonstrating high sensitivity to meteorological variability. Among these, solar radiation and temperature were the key meteorological factors. The seamless high-resolution O3 datasets will enable more insightful analyses regarding the spatiotemporal characterization and cause analysis.